The results are shown in Figure REF . As can be seen, DILOG outperforms other models on the overall test set, which shows DILOG is capable of doing one-shot learning and zero-shot domain transfer even under noisy data. Compared with the MLP policy trained with all the 526 samples in the restaurant domain (denoted as -526), DILOG trained with one sample has a higher inform/success rate but lower action F1 score. Noticeably, DILOG with the addition of missing slots in NLG achieves 91.40 and 90.20 inform and success rate overall, which is higher than the state-of-the-art DAMD.Note that DAMD is a more complex model with a higher capacity, which tends to overfit on extremely small datasets. Hence, MLP outperforms DAMD under the one-shot learning setting. However, when trained using all the dialogs in the restaurant domain, DAMD significantly outperformed the MLP when tested in-domain, as expected.  Also, we are training DAMD-526 solely on the 526 dialogs in Restaurant domain. This results in lower inform and success rates compared to DAMD trained on the entire MultiWoZ dataset, which are 89.2 and 77.9, respectively [1]}. This shows inform and success are incomplete metrics which do not penalize false positives. Hence, we add action F1 as an additional metric to complement inform and success. Note that action F1 cannot solely capture the performance either, since there may be multiple possible true actions in a given state.
